Cuicui Lu, Weining Wang, Jeffrey M. Wooldridge
arXiv 13 Oct 2018 · Econometrics · publishedEconometric Reviews (2024) · 4 citations (OpenAlex)
arXiv:1810.05855 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we study estimation of nonlinear models with cross sectional data using two-step generalized estimating equations (GEE) in the quasi-maximum likelihood estimation (QMLE) framework. In the interest of improving efficiency, we propose a grouping estimator to account for the potential spatial correlation in the underlying innovations. We use a Poisson model and a Negative Binomial II model for count data and a Probit model for binary response data to demonstrate the GEE procedure. Under mild weak dependency assumptions, results on estimation consistency and asymptotic normality are provided. Monte Carlo simulations show efficiency gain of our approach in comparison of different estimation methods for count data and binary response data. Finally we apply the GEE approach to study the determinants of the inflow foreign direct investment (FDI) to China.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Jenish \ Prucha (2009) Central limit theorems and uniform laws of large numbers for arrays of random fields, Journal of Econometrics 150(1): 86–98 | 0.909 | 8 | 3 | 75% |
| 2 | Jenish \ Prucha (2012) On spatial processes and asymptotic inference under near-epoch dependence, Journal of Econometrics 170(1): 178–190 | 0.888 | 10 | 3 | 70% |
| 3 | Wang, Iglesias \ Wooldridge (2013) Partial maximum likelihood estimation of spatial probit models, Journal of Econometrics 172(1): 77–89 | 0.811 | 4 | 2 | 100% |
| 4 | Gourieroux, Monfort \ Trognon (1984) Pseudo maximum likelihood methods: Theory, Econometrica: Journal of the Econometric Society pp. 681–700 | 0.737 | 3 | 2 | 100% |
| 5 | Liang \ Zeger (1986) Longitudinal data analysis using generalized linear models, Biometrika 73(1): 13–22 | 0.737 | 3 | 2 | 100% |
| 6 | Wooldridge (2010) Econometric analysis of cross section and panel data, MIT press self | 0.737 | 3 | 2 | 100% |
| 7 | Bloom, Schankerman \ Van Reenen (2013) Identifying technology spillovers and product market rivalry, Econometrica 81(4): 1347–1393 | 0.644 | 2 | 2 | 100% |
| 8 | Silva \ Tenreyro (2006) The log of gravity, The Review of Economics and statistics 88(4): 641–658 | 0.644 | 2 | 2 | 100% |
| 9 | Cressie (1992) Statistics for spatial data, Terra Nova 4(5): 613–617 | 0.511 | 2 | 1 | 100% |
| 10 | Kelejian \ Prucha (2007) HAC estimation in a spatial framework, Journal of Econometrics 140(1): 131–154 | 0.511 | 2 | 1 | 100% |
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